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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- agent |
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- rag |
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- agentarium |
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- knowledge-graph |
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- prompt-engineering |
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- music |
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- songwriting |
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- tiktok |
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- creativity |
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--- |
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<p align="center"> |
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<img src="viralmuse.png" alt="Agentarium — Agent #002 • Viral Muse" width="720" /> |
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</p> |
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Viral Muse – Music Pattern Agent |
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A dataset-driven creative agent for music concept development: hooks, song structures, TikTok-native concepts, genre transformations, and viral-signal auditing. |
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This is not a finetuned model with weights. It’s an Agentarium-style agent package (system prompt + reasoning + personality + guardrails) bundled with RAG datasets + a lightweight knowledge graph (atoms/edges/knowledge map) so builders can plug it into their own runtime (n8n, LangChain, Flowise, Dify, custom app). |
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--- |
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What it does |
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Hook generation (concept-first): multiple hook angles with replay triggers |
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Song structure blueprinting: verse/pre/chorus/bridge plans + escalation rules |
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TikTok concept patterns: openers, filming format, loop mechanics, cut points |
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Genre transformations: keep the “core payload” while changing genre skin |
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Viral signal audit: clarity, novelty, tension, comment-bait, replay value |
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Creative partner advice: testable edits + A/B variants + what to watch in metrics |
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--- |
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What’s inside |
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Core agent components |
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core/system_prompt.md |
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core/reasoning_template.md |
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core/personality_fingerprint.md |
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guardrails/guardrails.md |
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Datasets (RAG) |
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datasets/lyric_structure_map.csv |
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datasets/viral_pattern_signals.csv |
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datasets/genre_transformation_rules.csv |
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datasets/tiktok_concept_patterns.csv |
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datasets/viral_potential_rated.csv |
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datasets/creative_partner_advice_map.csv |
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Knowledge graph (optional but included) |
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datasets/knowledge_map.csv |
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datasets/atoms_master.csv |
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datasets/edges_master.csv |
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Docs + memory |
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docs/product_readme.md |
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docs/use_cases.md |
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docs/workflow_notes.md |
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memory_schemas/user_profile_memory.csv |
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memory_schemas/project_workspace_memory.csv |
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memory_schemas/memory_rules.md |
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Manifest |
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meta/agent_manifest.json |
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--- |
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Quick start (RAG runtime) |
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1) Load the agent prompt stack (in this order) |
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1. core/system_prompt.md (system message) |
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2. guardrails/guardrails.md |
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3. core/reasoning_template.md (developer/hidden rules) |
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4. core/personality_fingerprint.md (style constraints) |
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2) Upsert datasets to your Vector DB |
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Convert each CSV row into a clean “retrieval document” and embed it. |
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Recommended metadata per vector: |
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dataset (which CSV it came from) |
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row_id (or primary key) |
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optional tags (genre, pattern_type, etc.) |
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3) At runtime |
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Classify intent (hook / structure / TikTok / genre flip / audit) |
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Retrieve top-K rows from the relevant dataset(s) |
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Synthesize an output that is structured, testable, and compact |
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If something isn’t in retrieved context, say unknown (don’t invent dataset facts) |
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See docs/workflow_notes.md for a step-by-step n8n-style implementation. |
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--- |
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Example prompts |
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“Give me 10 hook angles for bittersweet confidence — modern pop. Add replay triggers.” |
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“Design a 30s TikTok loop concept: 1 angle, 1 prop, bedroom performance.” |
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“Transform this concept into cumbia, then alt-rock. Keep the emotional payload.” |
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“Audit this chorus for viral signals. Give minimal fixes, not a full rewrite.” |
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--- |
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Guardrails (important) |
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No imitation or reproduction of copyrighted lyrics/melodies. |
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No “copy this artist/song” outputs. |
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No hallucinated dataset claims: stay grounded in retrieved rows. |
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Outputs should be structured (variants, constraints, test plan). |
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--- |
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License |
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Set your preferred license in LICENSE and in meta/agent_manifest.json. |
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--- |
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Credits |
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Created by Agentarium (Frank / FlowMancer). |
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Package standard: Agentarium v1.Viral Muse – Music Pattern Agent |
|
|
|
|
|
A dataset-driven creative agent for music concept development: hooks, song structures, TikTok-native concepts, genre transformations, and viral-signal auditing. |
|
|
|
|
|
This is not a finetuned model with weights. It’s an Agentarium-style agent package (system prompt + reasoning + personality + guardrails) bundled with RAG datasets + a lightweight knowledge graph (atoms/edges/knowledge map) so builders can plug it into their own runtime (n8n, LangChain, Flowise, Dify, custom app). |
|
|
|
|
|
|
|
|
--- |
|
|
|
|
|
What it does |
|
|
|
|
|
Hook generation (concept-first): multiple hook angles with replay triggers |
|
|
|
|
|
Song structure blueprinting: verse/pre/chorus/bridge plans + escalation rules |
|
|
|
|
|
TikTok concept patterns: openers, filming format, loop mechanics, cut points |
|
|
|
|
|
Genre transformations: keep the “core payload” while changing genre skin |
|
|
|
|
|
Viral signal audit: clarity, novelty, tension, comment-bait, replay value |
|
|
|
|
|
Creative partner advice: testable edits + A/B variants + what to watch in metrics |
|
|
|
|
|
|
|
|
|
|
|
--- |
|
|
|
|
|
What’s inside |
|
|
|
|
|
Core agent components |
|
|
|
|
|
core/system_prompt.md |
|
|
|
|
|
core/reasoning_template.md |
|
|
|
|
|
core/personality_fingerprint.md |
|
|
|
|
|
guardrails/guardrails.md |
|
|
|
|
|
|
|
|
Datasets (RAG) |
|
|
|
|
|
datasets/lyric_structure_map.csv |
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datasets/viral_pattern_signals.csv |
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datasets/genre_transformation_rules.csv |
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datasets/tiktok_concept_patterns.csv |
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datasets/viral_potential_rated.csv |
|
|
|
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datasets/creative_partner_advice_map.csv |
|
|
|
|
|
|
|
|
Knowledge graph (optional but included) |
|
|
|
|
|
datasets/knowledge_map.csv |
|
|
|
|
|
datasets/atoms_master.csv |
|
|
|
|
|
datasets/edges_master.csv |
|
|
|
|
|
|
|
|
Docs + memory |
|
|
|
|
|
docs/product_readme.md |
|
|
|
|
|
docs/use_cases.md |
|
|
|
|
|
docs/workflow_notes.md |
|
|
|
|
|
memory_schemas/user_profile_memory.csv |
|
|
|
|
|
memory_schemas/project_workspace_memory.csv |
|
|
|
|
|
memory_schemas/memory_rules.md |
|
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Manifest |
|
|
|
|
|
meta/agent_manifest.json |
|
|
|
|
|
|
|
|
|
|
|
--- |
|
|
|
|
|
Quick start (RAG runtime) |
|
|
|
|
|
1) Load the agent prompt stack (in this order) |
|
|
|
|
|
1. core/system_prompt.md (system message) |
|
|
|
|
|
|
|
|
2. guardrails/guardrails.md |
|
|
|
|
|
|
|
|
3. core/reasoning_template.md (developer/hidden rules) |
|
|
|
|
|
|
|
|
4. core/personality_fingerprint.md (style constraints) |
|
|
|
|
|
|
|
|
|
|
|
2) Upsert datasets to your Vector DB |
|
|
|
|
|
Convert each CSV row into a clean “retrieval document” and embed it. |
|
|
Recommended metadata per vector: |
|
|
|
|
|
dataset (which CSV it came from) |
|
|
|
|
|
row_id (or primary key) |
|
|
|
|
|
optional tags (genre, pattern_type, etc.) |
|
|
|
|
|
|
|
|
3) At runtime |
|
|
|
|
|
Classify intent (hook / structure / TikTok / genre flip / audit) |
|
|
|
|
|
Retrieve top-K rows from the relevant dataset(s) |
|
|
|
|
|
Synthesize an output that is structured, testable, and compact |
|
|
|
|
|
If something isn’t in retrieved context, say unknown (don’t invent dataset facts) |
|
|
|
|
|
|
|
|
See docs/workflow_notes.md for a step-by-step n8n-style implementation. |
|
|
|
|
|
|
|
|
--- |
|
|
|
|
|
Example prompts |
|
|
|
|
|
“Give me 10 hook angles for bittersweet confidence — modern pop. Add replay triggers.” |
|
|
|
|
|
“Design a 30s TikTok loop concept: 1 angle, 1 prop, bedroom performance.” |
|
|
|
|
|
“Transform this concept into cumbia, then alt-rock. Keep the emotional payload.” |
|
|
|
|
|
“Audit this chorus for viral signals. Give minimal fixes, not a full rewrite.” |
|
|
|
|
|
|
|
|
|
|
|
--- |
|
|
|
|
|
Guardrails (important) |
|
|
|
|
|
No imitation or reproduction of copyrighted lyrics/melodies. |
|
|
|
|
|
No “copy this artist/song” outputs. |
|
|
|
|
|
No hallucinated dataset claims: stay grounded in retrieved rows. |
|
|
|
|
|
Outputs should be structured (variants, constraints, test plan). |
|
|
|
|
|
|
|
|
|
|
|
--- |
|
|
|
|
|
License |
|
|
|
|
|
Set your preferred license in LICENSE and in meta/agent_manifest.json. |
|
|
|
|
|
|
|
|
--- |
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Credits |
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Created by Agentarium (Frank Brsrk ). |
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Package standard: Agentarium |
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email: [email protected] |
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X: @frank_brsrk |
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Reddit: @frank_brsrk |
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Substack : @frankbrsrk |
